用自适应不确定性估计提升室内定位精度与可靠性
Spatially-Adaptive Conformal Graph Transformer for Indoor Localization in Wi-Fi Driven Networks
- 结合图注意力网络与空间自适应置信预测，建模信号与空间关系
- 在真实数据集上实现领先定位精度，且误差区域统计有效
- 适合对安全性和可靠性要求高的智能环境定位场景
室内定位是智能环境中导航、资产追踪及安全应用的关键技术。现有基于图的模型虽能利用无线局域网（Wi-Fi）接入点（AP）与设备间的空间关系实现高精度定位，但难以量化预测不确定性，限制了实际部署。本文提出空间自适应共形图变压器（SAC-GT），融合图变压器（GT）模型捕捉网络空间拓扑与信号强度动态，并引入新型空间自适应共形预测（SACP）方法，生成区域特异性不确定性估计。该框架不仅提供精确的二维（2D）位置预测，还生成符合统计置信度的置信区域，适配不同环境条件。在大规模真实数据集上的广泛评估表明，SAC-GT在保持领先定位精度的同时，提供了稳健且空间自适应的可靠性保障。
原文摘要 · Abstract (English)
Indoor localization is a critical enabler for a wide range of location-based services in smart environments, including navigation, asset tracking, and safety-critical applications. Recent graph-based models leverage spatial relationships between Wire-less Fidelity (Wi-Fi) Access Points (APs) and devices, offering finer localization granularity, but fall short in quantifying prediction uncertainty, a key requirement for real-world deployment. In this paper, we propose Spatially-Adaptive Conformal Graph Transformer (SAC-GT), a framework for accurate and reliable indoor localization. SAC-GT integrates a Graph Transformer (GT) model that captures network's spatial topology and signal strength dynamics, with a novel Spatially-Adaptive Conformal Prediction (SACP) method that provides region-specific uncertainty estimates. This allows SAC-GT to produce not only precise two-dimensional (2D) location predictions but also statistically valid confidence regions tailored to varying environmental conditions. Extensive evaluations on a large-scale real-world dataset demonstrate that the proposed SAC-GT solution achieves state-of-the-art localization accuracy while delivering robust and spatially adaptive reliability guarantees.
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